Infosys Certified Generative AI Professional Advanced
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Real Lex exam-pattern multiple-choice questions for the Infosys Certified Generative AI Professional Advanced certification. Each question includes the correct answer. The full question bank is available to Premium members.
- Question 1
Within the framework of Reinforcement Learning from Human Feedback, what is the primary role of human feedback within the learning process?
- ✓To guide the agents learning by providing a reward signal, shaping the agents behaviour indirectlyCorrect
- BTo create a ground truth dataset for supervised learning
- CTo define explicit rules and constraints of the environment, shaping the agents understanding of the task
- DTo serve as an alternative to a reward function, where humans directy score the agents performance on an continous scale
- Question 2
In Reinforcement Learning with Human Feedback which algorithmic approach is most frequently employed to fine-tune the policy based on the learned reward model derived from human feedback?
- ✓Q-learningCorrect
- BDeep Deterministic Policy Gradient (DDPG)
- CProximal Policy Optimization (PPO)
- DMonte Carlo Tree Search (MCTS)
- Question 3
Why is a comprehensive evaluation framework necessary for Large Language Models (LLMs)?
- ✓To make LLMs more complex and powerfulCorrect
- BTo improve the accuracy of LLMs
- CTo assess their safety, accuracy, reliability, and usability
- DTo avoid hallucinations
- Question 4
Which of the following statements accurately captures a key distinction between RLHF and traditional supervised learning in training language models?
- ✓RLHF uses both human feedback and rewards for training, while supervised learning relies only on labeled dataCorrect
- BRLHF does not involve any pre-training, unlike traditional supervised learning
- CRLHF requires significantly larger datasets than traditional supervised learning to be effective
- DRLHF eliminates the need for human feedback once the initial training phase is complete
- Question 5
Imagine a large language model tasked with generating a summary of a historical event. Despite producing fluent and grammatical correct text,the summary contains several factual inaccuracies not present in the original historical records.In the context of Large Language Models and Reinforcement Learning with Human Feedback, this phenomenon is best described as:
- ✓Bias amplificationCorrect
- BReward hacking
- CHallucination
- DOverfitting
- Question 6
A team is developing a large language model for customer service. They decide to use Reinforcement Learning with Human Feedback to fine-tune the LLM. However they quickly encounter a major roadblock. Which of the following best describes the most likely and significant obstacle they will face in the practical implementation of RLHF?
- ✓The inherent simplicity and automation of training the reward modelCorrect
- BThe ease and abundance of readily available, high-quality human feedback
- CThe substantial cost and logistical challenges associated with gathering sufficient amounts of reliable human feedback
- DThe guaranteed consistency and objectivity of human preferences across different individuals
- Question 7
A potential limitation of relying heavily on human feedback in RLHF, particularly when dealing with subjective or creative tasks is that
- ✓Human feedback is always perfectly objective and unbiasedCorrect
- BHuman preferences can be difficult to quantify and translate into a robust reward signal
- CHuman feedback is readily available in unlimited quantities and at no cost
- DHuman feedback is guaranteed to align perfectly with the long term goals of the AI system
- Question 8
A research team is evaluating the performance of two different language models that generate summaries of news articles and another set of language models that translate English text into Spanish. Which evaluation metric should the team use to assess the quality of the generated summaries, and which metric is more suitable for evaluating the machine translation models?
- ✓Use ROUGE scores for both summaries and machine translationCorrect
- BUse BLEU scores for both summaries and machine translation summaries and ROUGE scores for machine translation.
- CUse ROUGE scores for summaries and BLEU scores for machine translation.
- DUse BLEU scores for summaries and machine translation
- Question 9
What is the main objective of the ‘LogiQA’ benchmark for Large Language Models (LLMs) ?
- ✓To evaluate the language model's proficiency in generating creative contentCorrect
- BTo test the language model's translation capabilities for multiple languages.
- CTo measure the language model's fluency and coherence in generating text
- DTo assess the language model's ability to reason logically and solve complex problems
- Question 10
Identify the parameters that measures exactly the quantity that it is named after the average number of bits needed to encode on character.
- ✓PerplexityCorrect
- BBits-per-character (BPC)
- CMean Absolute Percentage Error
- DMean Squared Error
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